ISBN : 978-2-11-129254-3The integration capabilities offered by current nanoscale CMOS technologies enable the fabrication ofcomplete and very complex mixed-signal systems. However, manufacturing processes are prone to imperfections thatmay degrade –sometimes catastrophically– the intended functionality of the fabricated circuits. Extensive productiontests are then needed in order to separate these defective or unreliable parts from functionally correct devices.Unfortunately, the co-integration of blocks of very distinct nature (analog, mixed-signal, digital, RF, ...) as well as thelimited access to internal nodes in an integrated system make the test of these devices a very challenging and costlytask.BIST techniques have been proposed as a way to overcome these issues. These techniques aim at including some ofthe ATE functionality into the Device Under Test, in such a way that each fabricated system becomes self-testable.Applying BIST to the digital part of a complex integrated system is a common and standardized practice. Many testalternatives broadly proven in practice are available, all of them based on defect test and fault models. On the otherhand, AMS-RF BIST techniques are still lagging behind due to the strict requirements imposed by the analog circuitry.Since AMS-RF circuits are usually tested by measuring their functional specifications, this means that eachmeasurement has to comply with strict accuracy constraints to match the performance of the circuits under test.A promising solution to these issues is the combination of BIST strategies and machine learning-based tests. Machinelearning test strategies replace costly analog, mixed-signal and RF performance measurements by a set of simplermeasurements that can be performed on-chip by low-cost built-in test circuitry. The core idea is to build a mappingmodel from a set of simple measurements to the set of functional specifications. However, this test strategy is not freeof shortcomings either.My research has been focused on overcoming the limitations of current BIST and machine learning-based test forcomplex AMS-RF circuits, with the final goal of providing innovative state-of-the-art test solutions for these complexsystem
